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Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for;

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Cost-Sensitive Machine Learning

learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions;

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Cost-Sensitive Machine Learning

learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions;

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Big Data Preprocessing: Enabling Smart Data

and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on;

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Big Data Preprocessing

and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on;

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Machine Learning and Knowledge Discovery in Databases

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;

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Machine Learning and Knowledge Discovery in Databases

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;

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Machine Learning and Knowledge Discovery in Databases

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and;

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Biological Knowledge Discovery Handbook

comprehensive overview of data mining, preprocessing, postprocessing, and storage for biological data. It surveys the latest approaches and techniques;

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Life-Cycle Decisions for Biomedical Data

them. Life Cycle Decisions for Biomedical Data examines and assesses approaches and considerations for forecasting costs for preserving;

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Learning from Imbalanced Data Sets

focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for;

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Learning from Imbalanced Data Sets

focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for;

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Solving Large Scale Learning Tasks. Challenges and Algorithms

theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms;

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Feature Engineering for Machine Learning and Data Analytics

depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive;

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Feature Engineering for Machine Learning and Data Analytics

depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive;

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Hands-On Data Science for Biologists Using Python

Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day;

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Hands-On Data Science for Biologists Using Python

Hands-on Data Science for Biologists using Python has been conceptualized to address the massive data handling needs of modern-day;

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Marketing Research

Research Report; Epilogue. Each section of the book also has several hands-on cases, each chapter has “Ethical Dilemmas” for classroom debate, and;

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Data Mining for Bioinformatics Applications

preprocessing to modeling and result validation. Provides valuable information on the data mining methods have been widely used for solving;

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Proceedings of the International Conference on Big Data, IoT, and Machine Learning

This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and;

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Data Mining with Decision Trees

, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining;

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Data Mining

, the book covers the trends, applications, and research frontiers in data mining. Presents a comprehensive new chapter on deep;

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Statistics and Machine Learning Methods for EHR Data

covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and;

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Handbook of Missing Data Methodology

and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts;

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Data Preprocessing in Data Mining

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process;

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Bioconductor Case Studies

an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced;

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The Palgrave Handbook of Survey Research

This handbook is a comprehensive reference guide for researchers, funding agencies and organizations engaged in survey research. Drawing on;

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